Given the above results, it seems highly unlikely that such performance is due to temporal correlation in the EEG data. However, to be certain, we further investigate the extent to which our results may be due to temporal correlation, as argued by Li et al. in [1]}, rather than to class-level information encoded in the neural signals.
The neural signals recorded between each pair of classes, i.e.,
the BDB dataset, can help address this question. Since the neural data in response to the blank screen is equidistant in time from two classes, a strong temporal correlation would result in significantly greater than chance classification of that data as either the class before or the class after the blank screen. Thus, we verify whether a model trained on the block-design BDVE dataset would classify blank-screen segments as either the preceding or subsequent class. Finding near chance level classification accuracy here would indicate little to no impact of a temporal correlation.
To assess the temporal correlation we assign two class labels to each blank segment in the BDB dataset, corresponding to the preceding class and the following class. Then, for each of the models trained on the BDVE dataset and whose results are given in Table REF , we compute the classification accuracy of the BDB dataset as the ratio of blank segments classified as either one of the corresponding classes.
Results are shown in Table REF , and reveal that all methods are at or slightly above chance accuracy (i.e., 5%, since for each segment has two possible correct options out of the 40 classes). This seems to be a clear indication that temporal correlation in [2]}'s data is minimal, suggesting that block design experiments (when properly pre-processed) are suitable for classification studies.
<TABLE>